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Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling

机译:利用证据模型权重,对印度Garhwal喜马拉雅山部分地区滑坡敏感性进行空间预测

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Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment can be carried out in vast stretches of Himalaya in short time in order to assess the impact of development as well as climate change/variability. The resultant map can play a critical role in selecting areas for remedial measures for slope stabilisation as well planning for future development of the region.
机译:印度北部的Garhwal喜马拉雅山已成为喜马拉雅山滑坡发生最突出的热点之一,这主要归因于与造山过程,陡峭的地形和频繁发生的极端降水事件有关的地质原因。由于该地区有许多朝圣和旅游中心,因此每年有成千上万的人来此访问,并且在最近的过去,快速发展提供了充足的道路和基础设施。另外,还尝试通过建造隧道,水坝和水库来利用水力发电,从而改变许多地方的易损斜坡。结果,由滑坡灾害引起的总体风险增加了许多倍,因此,人们尝试使用“证据权重(WofE)”来评估滑坡的易感性。遥感和地理信息系统的方式。这种方法具有双重优势,因为它演示了如何在世界上数据稀缺的地区之一中得出与地质,地貌,坡度,土地利用以及最重要的是时间滑坡分布有关的关键参数。其次,它允许试验各种参数组合以评估其对滑坡的累积影响。根据高分辨率的印度遥感卫星数据(Cartosat-1和Resourcesat-1),总共准备了15个与地质,地貌,地形,水文和人为因素相关的参数以及2个不同的滑坡清单(2007年和2008-2011年之前)。 ),并通过现场调查进行了验证。使用WofE建模对参数进行了几种组合,最后使用八个参数的最佳组合,在易受滑坡发生的总面积的24%中,预测了总滑坡的76.5%。研究强调,使用这种方法,可以在短时间内在喜马拉雅山的广大地区进行滑坡敏感性评估,以评估发展的影响以及气候变化/多变性。生成的地图在选择用于稳定斜坡的补救措施以及规划该地区未来发展的区域时可以发挥关键作用。

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